Chemistry and Materials with the Amsterdam Modeling Suite

By Nicolas Onofrio

SCM, Amsterdam, Netherlands

Published on

Abstract

In this talk, I will give an overview of the Amsterdam Modeling Suite to perform atomistic simulations at various levels of theory. First, I will describe the AMS driver designed to explore potential energy surfaces of molecules and periodic systems, and its integration with our DFT, DFTB and force field engines. To illustrate the capability of our DFT engines, I will introduce a few studies performed with ADF and its periodic extension BAND. Second, I will focus on force fields (ReaxFF and MLPotential) to describe reactive systems and polymer chemistry. Finally, I will present PARAMS to create new force field parameters in order to describe novel molecular interactions. Along with this presentation, I will illustrate the various applications with short demos or videos performed with the AMS graphical user interface.

Bio

Nicolas Onofrio received his PhD from the department of Chemistry at the University of Grenoble Alpes in France followed by a Postdoc at Purdue University. Nicolas was an Assistant Professor in the department of Applied Physics at the Hong Kong Polytechnic University before joining SCM in 2023.

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Cite this work

Researchers should cite this work as follows:

  • Nicolas Onofrio (2023), "Chemistry and Materials with the Amsterdam Modeling Suite," https://nanohub.org/resources/37058.

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Time

Tags

Chemistry and Materials with the Amsterdam Modeling Suite
  • Chemistry & Materials with the Amsterdam Modeling Suite 1. Chemistry & Materials with the… 0
    00:00/00:00
  • Overview 2. Overview 84.18418418418419
    00:00/00:00
  • Background: ADF & SCM 3. Background: ADF & SCM 172.43910577243912
    00:00/00:00
  • From ADF to AMS 4. From ADF to AMS 271.43810477143813
    00:00/00:00
  • From ADF to AMS 5. From ADF to AMS 450.68401735068403
    00:00/00:00
  • The graphical user interface 6. The graphical user interface 510.77744411077748
    00:00/00:00
  • The ReaxFF engine 7. The ReaxFF engine 900.900900900901
    00:00/00:00
  • ReaxFF (1) 8. ReaxFF (1) 1049.94994994995
    00:00/00:00
  • ReaxFF (2) 9. ReaxFF (2) 1339.73973973974
    00:00/00:00
  • ReaxFF (3) 10. ReaxFF (3) 1788.9556222889557
    00:00/00:00
  • ReaxFF (3) 11. ReaxFF (3) 2126.4597931264598
    00:00/00:00
  • eReaxFF (4) 12. eReaxFF (4) 2148.9155822489156
    00:00/00:00
  • ParAMS 13. ParAMS 2327.1604938271607
    00:00/00:00
  • Machine Learning Potentials 14. Machine Learning Potentials 2656.38972305639
    00:00/00:00
  • Workflows 15. Workflows 2737.0704037370706
    00:00/00:00
  • PLAMS 16. PLAMS 2816.0160160160162
    00:00/00:00
  • Thank you 17. Thank you 2898.998998998999
    00:00/00:00